Recently, with the development of robot technology, mobile robots which can set the driving route by themselves and move have been developed. In order to allow such mobile robots to efficiently determine a position and move in a space, the mobile robot must be able to generate a map and recognize its own position in the space wherein it moves.
Mobile robots employ a gyroscope and a driving motor equipped with an encoder to drive by dead reckoning navigation, and generate a map by analyzing images taken using a camera provided in the upper portion. In this case, when an error is incurred in the driving information from the gyroscope and the encoder, image information obtained from the camera is utilized to correct the accumulated error.
However, location-based mobile robots which have been developed so far were developed under the assumption of movement on a two-dimensional plane using a monocular camera or laser scanner. However, when a monocular camera is used, it is difficult to determine the distance to a feature point. Therefore, as the error of the dead reckoning navigation is increased, increasingly many errors may be included in the position recognition results.
Further, since laser scanners are too expensive to apply to mobile robots, increasing numbers of studies have been performed in recent years for utilizing simultaneous localization and mapping (SLAM) technology, which recognizes a space using a stereo camera and updates the location.
Mobile robots based on SLAM technology perform the processes of extracting a corner feature point from an image, creating a map by restoring three-dimensional coordinates of the corner feature point, and recognizing a location.
It is very important for mobile robots to recognize a space and recognize their own position within the space. Since mobiles robot which do not employ the above-described technique have limited mobility and may provide a very limited type of service, mobile robots are being developed competitively.